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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Shape optimization with surface-mapped CPPNs
AU - Richards, Daniel Courtney
AU - Amos, Martyn
N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2017/6
Y1 - 2017/6
N2 - Shape optimization techniques are becoming increasingly important in design and engineering. This growing significance reflects the need to exploit advances in digital fabrication technologies, and the desire to create new types of surface designs for various engineering applications. Evolutionary algorithms offer several key advantages for shape optimization, but they can also be restricted, especially as design problems scale up in size. A key challenge for evolutionary shape optimization is to overcome these challenges in order to apply evolutionary algorithms to large-scale, "real-world" engineering problems. This paper presents a new evolutionary approach to shape optimization using what we call "surface-mapped CPPNs". Our method outperforms a state-of-the-art gradient-based method on a simple benchmark problem, and scales well as degrees of freedom are added to the design problem. Our results demonstrate that surface-mapped CPPNs offer practical ways of approaching large-scale, real-world engineering problems with evolutionary algorithms, opening up exciting new opportunities for engineering design.
AB - Shape optimization techniques are becoming increasingly important in design and engineering. This growing significance reflects the need to exploit advances in digital fabrication technologies, and the desire to create new types of surface designs for various engineering applications. Evolutionary algorithms offer several key advantages for shape optimization, but they can also be restricted, especially as design problems scale up in size. A key challenge for evolutionary shape optimization is to overcome these challenges in order to apply evolutionary algorithms to large-scale, "real-world" engineering problems. This paper presents a new evolutionary approach to shape optimization using what we call "surface-mapped CPPNs". Our method outperforms a state-of-the-art gradient-based method on a simple benchmark problem, and scales well as degrees of freedom are added to the design problem. Our results demonstrate that surface-mapped CPPNs offer practical ways of approaching large-scale, real-world engineering problems with evolutionary algorithms, opening up exciting new opportunities for engineering design.
U2 - 10.1109/TEVC.2016.2606040
DO - 10.1109/TEVC.2016.2606040
M3 - Journal article
VL - 21
SP - 391
EP - 407
JO - IEEE Transactions on Evolutionary Computation
JF - IEEE Transactions on Evolutionary Computation
SN - 1089-778X
IS - 3
ER -